The article discusses a two-phase approach for text classification that employs large language models (LLMs) to generate a taxonomy and subsequently classify text. In Phase 1, a taxonomy is established to categorize labels. Phase 2 utilizes LLMs to produce pseudo-labeled data, which enhances the training of more efficient classifiers, enabling real-time deployment on a large scale. Through these methods, the authors aim to improve label assignment accuracy and streamline the text classification process, ensuring it can handle extensive datasets effectively.
In this phase, we leverage large language models to obtain a 'pseudo-labeled' corpus set, facilitating the training of efficient classifiers at scale.
By prompting the LLM to infer primary and applicable labels, we create a representative training dataset that ensures more accurate and reliable text classification.
Collection
[
|
...
]